this post was submitted on 14 Jan 2024
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I agree that it has limits but there are things we could do to make it reasonably good. ChatGPT knows how to execute actions (such as calling an API or doing a web search). It could probably be made to store and look up information in a vector database, essentially giving it a long-term memory.
Given some smaller breakthroughs in performance and model size we could conceivably retrain the network on new input continuously, in order to incorporate new knowledge.
That's the thing, I don't think a database can work as a long term memory here. How would it work? Let's say you tell your AI girlfriend that Interstellar movie was so bad it made you vomit. What would it store in the DB? When would it look that info up? It would be even worse with specific events. Should it remember the exact date of each event perfectly like DB does? It would be unnatural. To actually simulate memory it should alter the model somehow and the scale of the change should be proportional to the emotional charge of the message. I think this is on a completely different level than current models.
Vector databases are relatively good at this kind of thing, because they can find records based on queries that are semantically close instead of just a lexical search. It would probably still make sense to split the information up in fragments such as e.g., "Interstellar movie," "watched on February 2nd, 2021," "made me vomit", and then connect those records to each other. GPTs are good at that kind of preprocessing. The idea would not be to store exact data such as timestamps and that's not how vector databases work, so recall would be more associative just like for humans (I can't ask you what movie you watched on Feburary 2nd, 2021 and expect an accurate reply either).
But you would have to do something like multiple steps of preprocessing with expanding search depth on each step and do it both ways: when recollecting and changing memories. Like if I say:
So you process the first input, find the relevant info in the 'memory' but then for the second one you have to recognize that this is regarding the same memory, understand the memory and alter it/append to it. It would get complicated really fast. We would need some AI memory management system to manage the memory for the AI. I'm sure it's technically possible but I think it will take another breakthrough and we won't see it soon.
Again, you will certainly hit limitations if you push it, but the example you give would work fine if you just append the added information to the database. A query for Interstellar would return both your original statements and the fact that you later said you lied about it, and all of these records are inserted into the GPT's context (short-term memory) when discussing that subject.
...i may be too green to see something here, but wouldn't simply saving month, year, topic, mood and quote enough? If AI needs everything formatted in certain input, run this through API. Teach AI to save only moments where user uses agitated language or smth and to periodically run checks if current convo allows for throwback, for example by topic, with advanced query when user asks if AI remembers something.
Then sell all this data for fat profit.
So imagine a convo:
1 years later:
Now the AI can find the meesage that said 'Interstellar' in the history but without any context. To know you were talking about the movie it would have to analyze the entire conversation again. And the emotional charge of the message can also change instantly:
What would the AI 'remember'? It would require some higher level of understanding of the conversation and the 'memories' would have to be updated all the time. It's just not possible to replicate with simple log.
Thanks for examples, now yeah, that's really ain't that simple...and hard af to foolproof. :/
It’s when people dive into this sort of memory stuff that I always remember: “oh yeah, this is why people call it a stochastic parrot.”
LLMs can do a lot. But without memory, they run into walls fast.